Static-Analysis on CuraSec

ARQ: LLM-based framework auto-refines CodeQL queries for C/C++

Mon, 24 Aug 2026 13:10:29 +0000

Engineer — Learn: Research showing an LLM-driven refinement loop can cut false positives and grow true positive rates by up to ~120% in CodeQL C/C++ queries without labeled datasets — worth tracking if your AppSec pipeline relies on CodeQL, but no action needed on running systems today.
SOC/IR — Skip
Leader — Skip

Mon, 27 Jul 2026 15:10:27 +0000

Engineer — Learn: Interesting research combining code slicing with LLM analysis to detect reentrancy and overflow in ERC-721 contracts, but no tooling release or actionable change to running systems today.
SOC/IR — Skip
Leader — Skip

Mon, 13 Jul 2026 14:30:14 +0000

Engineer — Learn: Academic research on grounded agentic reasoning for malware behavior reconstruction; no immediate engineering action, but the tri-grounding approach (domain, semantics, knowledge) is worth noting when evaluating LLM-assisted code-analysis tooling.
SOC/IR — Learn: Malaika’s behavior-reconstruction framing — connecting sparse program evidence to auditable behavioral conclusions — could inform how teams structure LLM-assisted malware triage workflows, though no detection or hunt action is available from this paper alone.
Leader — Skip

Mon, 13 Jul 2026 14:30:14 +0000

Engineer — Learn: Academic proposal for interpretable static PDF analysis using Tsetlin Machines; no tooling released or integrated into common pipelines, but the interpretability angle is worth tracking for teams building or evaluating ML-based malware classifiers.
SOC/IR — Learn: The interpretability feature could eventually improve analyst trust in ML-based PDF triage, but no detection rules, IOCs, or deployable tooling accompany this research paper.
Leader — Skip